Getting TidyTuesday data

A very handy way to get data from the original TidyTuesday repo is using its own package functions: data = tidytuesdayR::tt_load(2020, week = 29) and then we need to store the element of this list as the actual dataset as astronauts = data$astronauts so the head looks like:

Sometimes head() function doesn’t say to much, so lets use the glimpse function of the {tidyduesdayR} package to take a good look into the data:

  Rows: 1,277
  Columns: 24
  $ id                       <dbl> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 1…
  $ number                   <dbl> 1, 2, 3, 3, 4, 5, 5, 6, 6, 7, 7, 7, 8, 8, 9,…
  $ nationwide_number        <dbl> 1, 2, 1, 1, 2, 2, 2, 4, 4, 3, 3, 3, 4, 4, 5,…
  $ name                     <chr> "Gagarin, Yuri", "Titov, Gherman", "Glenn, J…
  $ original_name            <chr> "ГАГАРИН Юрий Алексеевич", "ТИТОВ Герман Сте…
  $ sex                      <chr> "male", "male", "male", "male", "male", "mal…
  $ year_of_birth            <dbl> 1934, 1935, 1921, 1921, 1925, 1929, 1929, 19…
  $ nationality              <chr> "U.S.S.R/Russia", "U.S.S.R/Russia", "U.S.", …
  $ military_civilian        <chr> "military", "military", "military", "militar…
  $ selection                <chr> "TsPK-1", "TsPK-1", "NASA Astronaut Group 1"…
  $ year_of_selection        <dbl> 1960, 1960, 1959, 1959, 1959, 1960, 1960, 19…
  $ mission_number           <dbl> 1, 1, 1, 2, 1, 1, 2, 1, 2, 1, 2, 3, 1, 2, 1,…
  $ total_number_of_missions <dbl> 1, 1, 2, 2, 1, 2, 2, 2, 2, 3, 3, 3, 2, 2, 3,…
  $ occupation               <chr> "pilot", "pilot", "pilot", "PSP", "Pilot", "…
  $ year_of_mission          <dbl> 1961, 1961, 1962, 1998, 1962, 1962, 1970, 19…
  $ mission_title            <chr> "Vostok 1", "Vostok 2", "MA-6", "STS-95", "M…
  $ ascend_shuttle           <chr> "Vostok 1", "Vostok 2", "MA-6", "STS-95", "M…
  $ in_orbit                 <chr> "Vostok 2", "Vostok 2", "MA-6", "STS-95", "M…
  $ descend_shuttle          <chr> "Vostok 3", "Vostok 2", "MA-6", "STS-95", "M…
  $ hours_mission            <dbl> 1.77, 25.00, 5.00, 213.00, 5.00, 94.00, 424.…
  $ total_hrs_sum            <dbl> 1.77, 25.30, 218.00, 218.00, 5.00, 519.33, 5…
  $ field21                  <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
  $ eva_hrs_mission          <dbl> 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.…
  $ total_eva_hrs            <dbl> 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.…

Astronauts

countries <- astronauts %>% 
  select(year_of_mission, total_number_of_missions, nationality) %>%
  group_by(year_of_mission, nationality) %>% 
  mutate(nationality = as_factor(nationality), 
         year_of_mission = as.integer(year_of_mission), 
         ) %>% 
  summarise(
    count = sum(total_number_of_missions)
  ) %>% 
  ungroup() %>% 
  group_by(nationality) %>% 
  mutate(cumsum = cumsum(count)) %>% 
  ungroup() %>% 
  group_by(year_of_mission) %>% 
  mutate(ordering = rank(cumsum, ties.method = "max")) %>% 
  ungroup()


plot <- countries %>% 
  ggplot(aes(ordering, group = nationality))  +
  geom_tile(aes(y = cumsum/2, 
                height = cumsum,
                width = 0.7), 
            alpha = 0.6) +
  geom_text(aes(y = 0, label = nationality), hjust = 1.1) +
  coord_flip(clip = "off", expand = FALSE) +
  labs(
    title = "{frame_time}",
    subtitle = "",
    y = "Cumulative number of mission per year",
    x = "",
    caption = "Data: TidyTuesday week 29. Plot: @Gaspardelanoche",
    fill = ""
  ) +
  theme_ipsum() +
  theme(
    plot.title = element_text(size = 20, face = "bold"),
    plot.subtitle = element_text(size = 18),
    axis.title.x = element_text(size = 18),
    axis.title.y = element_text(size = 18),
    axis.text.y = element_blank(),
    axis.text.x = element_text(size = 14),
    axis.ticks.y = element_blank(),
    plot.caption = element_text(size = 12),
    plot.margin = margin(3, 3, 3, 3, "cm"),
  ) +
  transition_time(year_of_mission) +
  ease_aes('cubic-in') 

animate(plot, nframes = 500, fps = 10, width = 600, height = 600, end_pause = 25)

Fig 1. Comparison of the different coffee ratings for the different cups across countries and its associate distribution coffee cups.